L2M – AI-Guided Haptic Robotics for Scalable Surgical Skill Training

Surgical trainees, our primary users in medical schools and teaching hospitals, face a critical barrier: limited access to consistent, expert-guided, hands-on practice. This limitation originates from the constraints of the conventional surgical training model, which depends heavily on the physical presence of expert surgeons. To address this limitation, we are constructing a robot-assisted surgical training platform that enables surgical residents to receive expert-level guidance and correction without constant presence of supervising surgeons. This product addresses core challenges in surgical education, including limited expert availability, duty hour restrictions for surgical residents, reduced hands-on exposure, and the lack of real-time, high-fidelity feedback. The system combines haptic robots with AI, to emulate the motor behavior of expert surgeons. Unlike conventional surgical simulators, which either lack meaningful feedback or depend on surgeons for assessment, our platform provides adaptive, context-aware, real-time guidance through force feedback and motion correction, effectively replicating an expert mentor’s role during hands-on training.

Faculty Supervisor:

Hossein Rouhani

Student:

Partner:

Edmonton Unlimited

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Public administration

University:

University of Alberta

Program: